Regularization in reinforcement learning has been used to perform effective exploration and learning generalized policies. For instance, [1]} uses mutual-information regularization to optimize a prior action distribution for better performance and exploration, [2]} regularizes the policy \(\pi (a|s)\)  using a control prior, [3]} uses temporal difference error regularization to reduce variance in Generalized Advantage Estimation [4]}. Generalization in reinforcement learning refers to the performance of the policy on different environment compared to the training environment. For example, [5]} studied the effect of \(L^2\)  norm on DQN on generalization, [6]} studied generalization between simulations vs. the real world, [7]} studied parameter variations and [8]} studied the effect of different random seeds in environment generation.
